Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters-Reference-Cited by-同舟云学术

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Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters

Published:2024-04-08 Issue: Volume: Page:
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Hassaan Said¹,Mohamed Abdulaziz¹,Ibrahim Ahmed Farid²³^ORCID,Elkatatny Salaheldin²³^ORCID

Affiliation:

1. Department of Petroleum Engineering, Cairo University, Giza 12613, Egypt

2. Department of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

3. Center for Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

Funder

College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.3c08795

Reference38 articles.

1. Enhanced Reservoir Description: Using Core and Log Data to Identify Hydraulic (Flow) Units and Predict Permeability in Uncored Intervals/Wells

2. Carbonate Reservoir Characterization

3. Hydrocarbon Effect Correction on Porosity Calculation from Density Neutron Logs Using Volume of Shale in Niger Delta

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing the Understanding of Subsurface Relations: Machine Learning Approaches for Well Data Analysis in the Drava Basin, Pannonian Super Basin;Applied Sciences;2024-07-10

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